Öznel iyi oluş modelini kullanarak stress ölçeğine dayalı akıl sağlığı tahmini

It has been decided that one of the most significant facets of existence is well-being, as this is the greatest way to keep people healthy and productive. This has led to the conclusion that well-being is one of the most important parts of existence. Because there are a substantial number of different ways to define "well-being," there are also a significant number of different scales that can be used to assess it. The purpose of the well-being scale is to provide general information on the level of an individual's quality of life. In this study, Subjective Well Being (SWB) characteristics are used in conjunction with machine learning classifiers to make predictions regarding levels of stress. In order to collect data from college students between the years of 2015 and 2019, sensors and self-reports were utilized, and approximately 700 students took part. To have a glance of understanding human nature and the requirements, it has has recently emerged as a popular topic of study among experts. Because of this, the combination of Artificial Intelligence (AI) and Psychology can appear to be straightforward; yet, in order to identify each of the aspects and their reflections in AI's research, the subject of psychology needs to be thoroughly researched. The purpose of this study is to provide individuals with a basic framework for understanding their physical and mental health situations by presenting and explaining in detail the findings of a psychological research. A growing number of studies have been carried out to investigate the possibility of accurately predicting a person's level of happiness by making use of carefully constructed models. In order to build a Subjective Well-Being (SWB) model that has any chance of success, it is necessary to conduct research into the histories of the features. We have selected the variables from the literature on SWB that are appropriate for the real-world data instructions, and these variables come from the suitable categories. The purpose of this work is to evaluate the model by providing it with SWB characteristics and then classifying the different levels of stress using machine learning methods in order to determine how well the model functions when applied to a real dataset. We have achieved significant metric scores, which may be taken into account for a particular task, despite the fact that it is a multiclass classification issue.